The invention relates to changing the size of images and more particularly to a hybrid bilinear scaling system that provides high quality output images.
The process of changing the size of an image is commonly referred to as scaling. There are several well-defined and well-understood methods used for image scaling. An overview of the subject is described in A Simplified Approach to Image Processing, R. Crane, Prentice Hall, Inc., 1997.
Scaling methods range from trivial, pixel-replication techniques to more complex, higher order algorithms. When selecting a particular scaling technique for a given application, a tradeoff is made between computational complexity and resultant image quality. For applications with stringent computational limits or loose image quality requirements, pixel-replication is a popular method used for scaling. Conversely, if an application can afford higher computational costs, or if there are tighter constraints on the output image quality, more complex scaling schemes are used. The most common alternative to pixel replication is bilinear interpolation, which provides improvement in image quality at a moderate computational expense.
With bilinear interpolation, new or scaled pixels are computed as a weighted sum of neighboring pixels. Weights are computed linearly and proportionally to the distance the new pixel is to existing or neighboring pixels. The new pixel locations, relative to the input (original) pixels, are determined by reverse-mapping the desired, destination pixels back to the input space. In most cases, the original input pixels are not used in the resultant output image. The only time input pixels are directly mapped to an output pixel is when the reverse-mapping of the output pixel location happens to land exactly on a grid location in the original input space. Thus, techniques, like bilinear interpolation, are computationally exhaustive and require complex scaling circuitry. Accordingly, a need remains for a simple scaling technique that produces high image quality.